Generative artificial intelligence supporting image and document enhancements for training models using quantum computing

Abstract

A system for the enhancement of a digital image using quantum computing to provide an organization with the enhanced digital image for further use. A quantum computer may be configured to receive digital images that include pixels, determine whether the digital images comprise a PPI that is below a PPI threshold, convert the pixels of the digital image into qubits of the digital image when below the PPI threshold, use quantum superposition properties and/or quantum entanglement properties of qubits to propose additional pixels for enhancement of the digital image, and run a GenAI model to confirm the accuracy of the additional pixels proposed to enhance the digital image. Upon receiving confirmation from the GenAI model, add the additional pixels to enhance the digital image to exceed the PPI threshold, and provide the converted digital image to the organization.

What is claimed is: 
     
         1 . A system for enhancement of digital scans of documents using quantum computing to support training an artificial intelligence (“AI”) model, the system comprising: 
 a quantum computer, said quantum computer for use in enhancing digital scans to prepare a digital scan for use in training an AI model; 
 a generative artificial intelligence (“GenAI”) model, said GenAI model for providing confirmation of accuracy of enhancements made to digital scans by the quantum computer; 
 wherein the quantum computer is configured to enhance digital scans of documents when said digital scans comprise at least one corrupt pixel and/or at least one missing pixel for each digital scan, and enhancing digital scans comprise correcting the at least one corrupt pixel and/or at least one missing pixel for each digital scan, said quantum computer being configured to: 
 receive digital scans of documents, said digital scans comprising pixels;  
 determine for each digital scan whether the digital scan comprises at least one corrupt pixel and/or at least one missing pixel;  
 for each digital scan that comprises at least one corrupt pixel and/or at least one missing pixel, convert the pixels of the digital scan into qubits of the digital scan, 
 propose, using quantum superposition properties and/or quantum entanglement properties of qubits, one or more pixels to enhance the digital scan; 
 run GenAI model to confirm an accuracy of the one or more pixels proposed to enhance the digital scan using quantum superposition properties and/or quantum entanglement properties of qubits;  
 when receiving confirmation from the GenAI model, convert the digital scan that comprise at least one corrupt pixel and/or at least one missing pixel into a digital scan that comprises no corrupt pixels and no missing pixels by updating the digital scan with the one or more pixels proposed using quantum superposition properties and/or quantum entanglement properties of qubits; and 
 after updating one or more digital scans with one or more pixels, provide the one or more digital scans that comprise no corrupt pixels and no missing pixels to train the AI model. 
 
 
     
     
         2 . The system of  claim 1  wherein the GenAI model comprises a generative adversarial network (“GAN”) model, a variational autoencoders (VAE) model, and/or a diffusion model. 
     
     
         3 . The system of  claim 1  wherein the AI model and the GenAI model are different models. 
     
     
         4 . The system of  claim 1  wherein:  
       the AI model and the GenAI model are different models; 
       the GenAI model is a first GenAI mode; and 
       the AI model is a second GenAI model. 
     
     
         5 . The system of  claim 1  wherein the documents comprise a quality level that when scanned generates a digital scan with a low resolution. 
     
     
         6 . The system of  claim 1  where said quantum computer is further configured to: 
 maintain a log of changes made to a digital scan; and 
 revert to a previous version of the digital scan when an error is discovered by the GenAI model. 
 
     
     
         7 . A system for enhancement of electronic documents using quantum computing to support training an artificial intelligence (“AI”) model, the system comprising: 
 a quantum computer, said quantum computer for use in enhancing electronic documents for use in training an AI model; 
 a generative artificial intelligence (“GenAI”) model, said GenAI model for providing confirmation of accuracy of enhancements made to electronic documents by the quantum computer; 
 wherein the quantum computer is configured to enhance electronic documents when said electronic documents comprise a quality that is below a quality threshold, said quality that is below the quality threshold comprising at least one corrupt character and/or at least one missing character, and enhancing electronic documents comprise correcting the at least one corrupt character and/or at least one missing character for each electronic document, said quantum computer being configured to: 
 receive electronic documents, said electronic documents comprising characters;  
 determine whether the electronic documents comprise a quality that is below the quality threshold;  
 for each electronic document that comprises a quality that is below the quality threshold, convert the characters of the electronic document into qubits of the electronic document; 
 propose, using quantum superposition properties and/or quantum entanglement properties of qubits, one or more characters to enhance a quality of the electronic document; 
 run GenAI model to confirm an accuracy of the one or more characters proposed to enhance the quality of the electronic document using quantum superposition properties and/or quantum entanglement properties of qubits;  
 when receiving confirmation from the GenAI model, convert the electronic document with a quality that is below the quality threshold into an electronic document with a quality that is above the quality threshold by updating the electronic document with the one or more characters proposed using quantum superposition properties and/or quantum entanglement properties of qubits, where the electronic document with a quality that is above the quality threshold comprises no corrupt characters and no missing characters; and 
 provide one or more electronic documents converted from a quality that is below the quality threshold to a quality that is above the quality threshold to train the AI model. 
 
 
     
     
         8 . The system of  claim 7  wherein the GenAI model comprises a generative adversarial network (“GAN”) model, a variational autoencoders (VAE) model, and/or a diffusion model. 
     
     
         9 . The system of  claim 7 , wherein the AI model and the GenAI model are different models. 
     
     
         10 . The system of  claim 7  wherein:  
       the AI model and the GenAI model are different models; 
       the GenAI model is a first GenAI mode; and 
       the AI model is a second GenAI model. 
     
     
         11 . The system of  claim 7  where said quantum computer is further configured to: 
 maintain a log of changes made to an electronic document; and 
 revert to a previous version of the electronic document when an error is discovered by the GenAI model. 
 
     
     
         12 . A system for enhancement of a digital image using quantum computing to provide an organization with an enhanced digital image, the system comprising: 
 a quantum computer, said quantum computer for use in enhancing digital images to prepare the digital images for further use;   a generative artificial intelligence (“GenAI”) model, said GenAI model for providing confirmation of accuracy of enhancements made to digital images by the quantum computer;   wherein the quantum computer is configured to enhance digital images when said digital images comprise a pixels per inch (“PPI”) measurement that is less than a PPI threshold, and enhancing digital images comprise increasing PPI by adding at least one pixel for each digital image, said quantum computer being configured to: 
 receive digital images, said digital images comprising pixels;  
 determine whether the digital images comprise a PPI that is below the PPI threshold;  
 for each digital image that comprises a PPI that is below the PPI threshold, convert the pixels of the digital image into qubits of the digital image; 
 propose, using quantum superposition properties and/or quantum entanglement properties of qubits, one or more additional pixels to enhance the PPI of the digital image; 
 run GenAI model to confirm an accuracy of the one or more additional pixels proposed to enhance the PPI of the digital image using quantum superposition properties and/or quantum entanglement properties of qubits;  
 when receiving confirmation from the GenAI model, convert the digital image with a PPI that is below the PPI threshold into a digital image with a PPI that is above the PPI threshold by updating the digital image with the one or more additional pixels proposed using quantum superposition properties and/or quantum entanglement properties of qubits; and 
 provide to the organization one or more digital images converted from a PPI that is below the PPI threshold to a PPI that is above the PPI threshold. 
   
     
     
         13 . The system of  claim 12  wherein the GenAI model comprises a generative adversarial network (“GAN”) model, a variational autoencoders (VAE) model, and/or a diffusion model. 
     
     
         14 . The system of  claim 12  where said quantum computer is further configured to: 
 maintain a log of changes made to a digital image; and 
 revert to a previous version of the digital image when an error is discovered by the GenAI model. 
 
     
     
         15 . The system of  claim 12  wherein the PPI threshold is 68 PPI. 
     
     
         16 . The system of  claim 12  wherein the PPI threshold is 92 PPI. 
     
     
         17 . The system of  claim 12  wherein the PPI threshold is 175 PPI. 
     
     
         18 . The system of  claim 12  wherein the PPI threshold is 235 PPI. 
     
     
         19 . The system of  claim 12  wherein the PPI threshold is 295 PPI. 
     
     
         20 . The system of  claim 12  wherein the PPI threshold is 395 PPI.